Default DL85Booster

######################################################################
#                     DL8.5 boosting classifier                      #
######################################################################
<<=== Optiboost ===>>
Model building...
Model built. Duration of building = 4.0266
Number of trees = 21
Confusion Matrix below
[[ 18  16]
 [ 11 118]]
Accuracy DL85Booster + MODEL_LP_DEMIRIZ on training set = 0.8798
Accuracy DL85Booster + MODEL_LP_DEMIRIZ on test set = 0.8344

<<=== AdaBoost + CART ===>>
Model building...
/home/docs/.asdf/installs/python/3.9.15/lib/python3.9/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
Model built. Duration of building = 0.0489
Confusion Matrix below
[[ 19  15]
 [  7 122]]
Accuracy AdaBoost on training set = 0.8659
Accuracy AdaBoost on test set = 0.865



<<=== Optiboost ===>>
Model building...
Model built. Duration of building = 5.8645
Number of trees = 47
Confusion Matrix below
[[ 14  20]
 [  6 123]]
Accuracy DL85Booster + MODEL_LP_RATSCH on training set = 0.8367
Accuracy DL85Booster + MODEL_LP_RATSCH on test set = 0.8405

<<=== AdaBoost + CART ===>>
Model building...
/home/docs/.asdf/installs/python/3.9.15/lib/python3.9/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
Model built. Duration of building = 0.1268
Confusion Matrix below
[[ 20  14]
 [  6 123]]
Accuracy AdaBoost on training set = 0.8875
Accuracy AdaBoost on test set = 0.8773



<<=== Optiboost ===>>
Model building...
Model built. Duration of building = 13.4156
Number of trees = 34
Confusion Matrix below
[[ 16  18]
 [  4 125]]
Accuracy DL85Booster + MODEL_QP_MDBOOST on training set = 0.8844
Accuracy DL85Booster + MODEL_QP_MDBOOST on test set = 0.865

<<=== AdaBoost + CART ===>>
Model building...
/home/docs/.asdf/installs/python/3.9.15/lib/python3.9/site-packages/sklearn/ensemble/_base.py:166: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.
  warnings.warn(
Model built. Duration of building = 0.0783
Confusion Matrix below
[[ 20  14]
 [  5 124]]
Accuracy AdaBoost on training set = 0.8844
Accuracy AdaBoost on test set = 0.8834

import time
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, confusion_matrix
from sklearn.ensemble import AdaBoostClassifier
from sklearn.tree import DecisionTreeClassifier
from pydl85 import DL85Booster, Boosting_Model

dataset = np.genfromtxt("../datasets/anneal.txt", delimiter=' ')
X, y = dataset[:, 1:], dataset[:, 0]

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

models = [Boosting_Model.MODEL_LP_DEMIRIZ, Boosting_Model.MODEL_LP_RATSCH, Boosting_Model.MODEL_QP_MDBOOST]
regulators = [15] * len(models)
model_names = ['MODEL_LP_DEMIRIZ', 'MODEL_LP_RATSCH', 'MODEL_QP_MDBOOST']
depth = 1

print("######################################################################\n"
      "#                     DL8.5 boosting classifier                      #\n"
      "######################################################################")
for i, model in enumerate(models):
    print("<<=== Optiboost ===>>")
    db_clf = DL85Booster(max_depth=depth, regulator=regulators[i], model=model)
    start = time.perf_counter()
    print("Model building...")
    db_clf.fit(X_train, y_train)
    duration = time.perf_counter() - start
    print("Model built. Duration of building =", round(duration, 4))
    print("Number of trees =", db_clf.n_estimators_)
    y_pred = db_clf.predict(X_test)
    print("Confusion Matrix below")
    print(confusion_matrix(y_test, y_pred))
    print("Accuracy DL85Booster +", model_names[i], "on training set =", round(accuracy_score(y_train, db_clf.predict(X_train)), 4))
    print("Accuracy DL85Booster +", model_names[i], "on test set =", round(accuracy_score(y_test, y_pred), 4), "\n")

    print("<<=== AdaBoost + CART ===>>")
    ab_clf = AdaBoostClassifier(base_estimator=DecisionTreeClassifier(max_depth=depth), n_estimators=db_clf.n_estimators_)
    start = time.perf_counter()
    print("Model building...")
    ab_clf.fit(X_train, y_train)
    duration = time.perf_counter() - start
    print("Model built. Duration of building =", round(duration, 4))
    y_pred = ab_clf.predict(X_test)
    print("Confusion Matrix below")
    print(confusion_matrix(y_test, y_pred))
    print("Accuracy AdaBoost on training set =", round(accuracy_score(y_train, ab_clf.predict(X_train)), 4))
    print("Accuracy AdaBoost on test set =", round(accuracy_score(y_test, y_pred), 4))
    print("\n\n")